With the development of the computer and image processing technologies, face recognition is more and more used in security systems, interactive video applications, image editing and archiving applications, and computer vision applications, etc.
One challenging problem in face recognition is deducing a subject's identity through a provided image. Research efforts have been made on addressing practical large-scale face recognition systems in uncontrolled environments. Recently, face recognition via sparse representation-based classification (SRC) and its extensions may provide improved performance.
The SRC is based on the concept that a subject's face sample can be represented as a sparse linear combination of available images of the same subject captured under different conditions (e.g., poses, lighting conditions, etc.). The same principle can also be applied when a face image itself is represented in a lower dimensional space describing important and easily identifiable features. In order to enforce sparsity, l1 optimization algorithms can be used. Then, the face class that yields the minimum reconstruction error is selected in order to classify or identify the subject.
However, the sparse representation based face recognition often assumes that the training images are carefully controlled and that the number of samples per class is sufficiently large. In order to overcome the limitation of requiring large amounts of samples per class, this disclosure uses a sparsity-based approach combined with additional, more informative, least-squares steps to provide significant performance improvements with little additional cost.